IT Research Scientist (Generative Models) for an AI and Machine Learning Company

Purpose

The AI Research Group in the Office of the CTO is focused on discovering new, innovative experiences for designers. We have been working on a large-scale research project, trying to use machine learning and deep learning to redefine industrial projects for people who work in the finance, telcos, engineering or construction (AEC) industry. We believe that human-AI interaction and collaboration can create a paradigm shift for the users to design and create better results more efficiently.

Currently, we have several focused research directions, and Generative Adversarial Networks (GANs) is one of the most important. We have looked into numerous GAN structures and applied them for different purposes, and the rich results triggered us to further investigate the potential of it, including the fundamental theories and applications, e.g., generating variations, multi-representation translations, and GAN for data types other than images. Therefore, we are seeking for an expert on generative models (GANs and VAEs - Variation AutoEncoders) who is interested in exploring this new territory with us.


Responsibilities

  1. Research, invent, and implement novel machine learning algorithms and systems for generative models including GANs and VAEs
  2. Build industrial ai solution for deep-learning-assisted platform with a cross-disciplinary team, including designers, big data developers, deep learning researchers and interns
  3. Keep current with latest trends and technologies to help future research subjects and anticipate development needs
  4. Bonus to write technical papers to top-tier conferences such as NIPS, CVPR, ICML, ICCV, ECCV, and SIGGRAPH


Requirements

  1. Top-tier publication records, including but not limited to NIPS, CVPR, ICML, ICCV, ECCV, and SIGGRAPH
  2. Hands-on experience in Machine Learning, Deep Learning, Artificial Intelligence, and Data Science and their frameworks, such as Tensorflow, Pytorch, and Caffe
  3. Broad and deep understanding of the rapid development of machine learning and deep learning research
  4. Ability to quickly adapt to new situations and to learn new technologies
  5. Ability to collaborate and communicate effectively with a multicultural local and remote team
  6. Knowledge of deep learning algorithms is a plus
  7. Interests in Reinforcement learning is a plus
  8. Knowledge of 2D and 3D computer graphics and visualization techniques is a plus
  9. Experience with HPC, GPU computing, cloud computing and parallel programming is a plus